feat(tagging): SigLIP concept crops + max-over-bag scoring (#114)

Lift recall on small/local concepts (glasses, cum, stomach-bulge, xray,
lactation) that the whole-image SigLIP vector washes out: the GPU agent now
embeds figure crops with SigLIP too, stored as kind='concept' regions, and the
suggestion rail scores each image as a BAG (whole-image + every concept crop),
taking each head's MAX over the bag. The whole-image vector is always in the
bag, so this can never score lower than before.

Model-agnostic by construction: the server ANNOUNCES the embedding model
(HF name + version) in the lease, so the agent loads whatever the heads were
trained in and stays in lock-step — a model swap is a server setting + a
re-embed migration, never an agent change.

- agent: model-agnostic CropEmbedder (torch/transformers get_image_features,
  fp16 on CUDA, inference-locked); worker branches on job.task — 'ccip' emits
  figure(CCIP)+concept(SigLIP) in one pass, 'siglip' emits concept-only so the
  back-catalogue backfill never churns figure/CCIP regions; torch cu124 +
  transformers in the image.
- server: lease announces embed_model_name/embed_version; score_image is
  max-over-bag (version-filtered region embeddings); enqueue_gpu_backfill
  'siglip' gates on a missing concept region (drains the back-catalogue,
  retries failures, no double-enqueue); daily siglip-backfill beat; UI button;
  /api/ccip/overview reports images_with_concept_siglip.
- v1 scope: suggestion rail only — auto-apply stays whole-image (conservative;
  heads' thresholds were calibrated on whole-image). Bulk-apply bag = follow-up.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

frontend/src/components/settings/GpuAgentCard.vue

@@ -61,6 +61,16 @@
       processes until the agent is running.
     </p>
 
+    <v-btn
+      class="mt-3" color="accent" variant="tonal" rounded="pill" size="small"
+      prepend-icon="mdi-crop" :loading="backfillingSiglip" @click="onBackfillSiglip"
+    >Queue concept crops (SigLIP)</v-btn>
+    <p class="fc-muted text-caption mt-2 mb-0">
+      Enqueues every image that doesn't have concept-crop embeddings yet — the
+      localized vectors that help small/local tags (glasses, etc.) surface. New
+      images get these automatically; this catches the back-catalogue.
+    </p>
+
     <!-- Match strictness -->
     <div class="fc-section-h mt-5 mb-1">Character-match strictness</div>
     <div v-if="ml.settings" class="d-flex align-center" style="gap:12px">
@@ -115,6 +125,7 @@ const tokenValue = ref(null)
 const masked = ref(true)
 const rotating = ref(false)
 const backfilling = ref(false)
+const backfillingSiglip = ref(false)
 const threshold = ref(0.85)
 const savingThreshold = ref(false)
 const autoApply = ref(true)
@@ -215,6 +226,19 @@ async function onBackfill() {
     backfilling.value = false
   }
 }
+
+async function onBackfillSiglip() {
+  backfillingSiglip.value = true
+  try {
+    await store.backfill('siglip')
+    toast({ text: 'Queued concept crops — run the agent to process them', type: 'success' })
+    await refreshQueue()
+  } catch (e) {
+    toast({ text: `Could not queue backfill: ${e.message}`, type: 'error' })
+  } finally {
+    backfillingSiglip.value = false
+  }
+}
 </script>
 
 <style scoped>